plot_fs.py

import numpy as np
from matplotlib import pyplot as plt

def plot_fs(predict_func, X, y, resolution = .01, save_name=None):
    # Plot the decision boundary. For that, we will assign a color to each
    # point in the mesh [x_min, x_max]x[y_min, y_max].
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    xx, yy = np.meshgrid(np.arange(x_min, x_max, resolution),
                         np.arange(y_min, y_max, resolution))
    Z = predict_func(np.c_[xx.ravel(), yy.ravel()])
    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure(1, figsize=(4, 3))
    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Paired)
    plt.xlabel('Sepal length')
    plt.ylabel('Sepal width')

    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.xticks(())
    plt.yticks(())

    if save_name is not None:
        plt.savefig(save_name)
    plt.show()